Benchmark Dataset For Mathematical Reasoning

via “competition-mathematics problem dataset loading with multi-subject stratification”

12.5K competition math problems — AMC/AIME/Olympiad level, 7 subjects, standard math benchmark.

Unique: Curates problems exclusively from high-difficulty mathematical competitions (AMC, AIME, Olympiads) rather than generic math word problems, ensuring evaluation on reasoning-intensive problems that require multi-step derivations and deep mathematical understanding. The MATHDataset class implements subject-aware stratification enabling fine-grained evaluation across mathematical domains.

vs others: More rigorous than generic math QA datasets (e.g., MathQA, SVAMP) because problems require genuine mathematical reasoning rather than simple arithmetic, making it the de facto standard for evaluating LLM mathematical capabilities in research.

via “visual mathematical dataset curation and annotation”

Visual mathematical reasoning benchmark.

Unique: Newly created datasets (IQTest, FunctionQA, PaperQA) are purpose-built for compositional visual-mathematical reasoning rather than repurposed from general vision-language tasks. Includes auxiliary annotations (OCR, captions) enabling evaluation of text-only models as baselines, revealing how much visual understanding contributes to performance vs. text-based reasoning alone.

vs others: More comprehensive than single-source mathematical reasoning datasets because it aggregates 28 existing datasets plus 3 new ones, providing broader coverage of visual mathematical domains and reducing bias from any single source's annotation style or problem distribution.

via “cross-subdiscipline mathematical reasoning measurement”

Expert-level math problems created by mathematicians.

Unique: Explicitly structures evaluation across four mathematical subdisciplines (number theory, algebra, geometry, analysis) to measure generalization and identify domain-specific reasoning patterns, rather than treating mathematics as a monolithic domain

vs others: Provides subdiscipline-specific performance insights that reveal whether AI reasoning is broadly generalizable or domain-dependent, whereas most benchmarks report aggregate mathematical performance

via “benchmark dataset for evaluating language model reasoning”

23 hardest BIG-Bench tasks where models initially failed.

Unique: Specifically curated to challenge language models on reasoning tasks rather than knowledge retrieval, making it unique in its focus.

vs others: Offers a more rigorous evaluation of reasoning capabilities compared to standard datasets that focus primarily on knowledge retrieval.

via “mathematical reasoning with math benchmark performance”

Meta's 70B open model matching 405B-class performance.

Unique: Achieves strong mathematical reasoning performance at 70B parameters through instruction-tuning on mathematical problem-solving datasets, enabling competitive MATH benchmark performance without specialized symbolic reasoning modules

vs others: Provides mathematical reasoning capability comparable to larger closed-source models while remaining open-weight and self-hostable, though without formal verification guarantees of symbolic math systems

via “scientific reasoning benchmark dataset”

7.8K science questions testing genuine reasoning, not just recall.

Unique: This dataset uniquely challenges AI models with questions that require genuine scientific reasoning rather than simple retrieval or memorization.

vs others: It stands out from other datasets by focusing specifically on the application of scientific knowledge in novel contexts.

via “benchmark-validated reasoning performance on standardized datasets”

Alibaba's 32B reasoning model with chain-of-thought.

Unique: Provides documented benchmark results on standardized reasoning datasets (AIME 79.5%, MATH-500 96.4%) enabling quantitative performance validation, with explicit comparison claims against larger models

vs others: Demonstrates competitive reasoning performance on standardized benchmarks comparable to much larger models, providing quantitative evidence of reasoning capability for evaluation and comparison purposes

via “mathematical reasoning and step-by-step problem solving”

DeepSeek's 236B MoE model specialized for code.

Unique: Trained on 6 trillion tokens including mathematical reasoning datasets and code-based solutions, enabling both symbolic reasoning and code generation for mathematical problems in a single model without separate math-specific components

vs others: Provides integrated mathematical reasoning and code generation (unlike Copilot which focuses on code) while maintaining open-source weights and supporting local deployment

via “reasoning and chain-of-thought decomposition for complex tasks”

Google's open-weight model family from 1B to 27B parameters.

Unique: 27B variant achieves reasoning performance competitive with much larger models (70B+) through optimized training on reasoning-heavy datasets and learned chain-of-thought patterns, without requiring external reasoning engines or symbolic solvers

vs others: Outperforms Llama 2 70B on math and coding reasoning benchmarks while being 2.6x smaller, and matches Mistral 7B on reasoning tasks while offering superior code generation quality

via “mathematical reasoning with math benchmark 80+ and structured problem-solving”

Alibaba's 72B open model trained on 18T tokens.

Unique: Integrates three distinct reasoning paradigms (CoT for symbolic reasoning, PoT for code-based computation, TIR for external tool orchestration) within single 72B dense model, enabling flexible problem-solving strategies without model switching. 128K context window allows full problem histories and solution verification within single inference call.

vs others: Outperforms Llama 2 70B (significantly lower math performance) and matches Llama 3 70B on general benchmarks while offering specialized math reasoning patterns; Qwen2.5-Math 72B variant provides deeper specialization but general-purpose 72B enables seamless math-to-code-to-text transitions without model switching.

via “mathematical reasoning and problem-solving”

671B MoE model matching GPT-4o at fraction of training cost.

Unique: Achieves 90.2% on MATH benchmark through MoE architecture that routes mathematical reasoning tokens through specialized expert parameters, enabling efficient scaling of reasoning capability without proportional increase in active parameters per token

vs others: Matches GPT-4o mathematical reasoning performance (90.2% MATH) while using 37B active parameters vs GPT-4o's undisclosed parameter count, reducing inference latency and cost for math-heavy workloads

via “mathematical reasoning with 96.8% gsm8k accuracy”

Largest open-weight model at 405B parameters.

Unique: 405B parameter scale enables 96.8% GSM8K performance through learned chain-of-thought patterns in transformer architecture, achieving near-human accuracy on grade-school math without external symbolic engines or calculators

vs others: Larger model scale than most open-source alternatives improves mathematical reasoning accuracy; however, lacks symbolic verification that specialized math engines provide, making it suitable for reasoning tasks but not formal proofs

12.5K competition math problems across 7 subjects and 5 difficulty levels.

Unique: This dataset includes detailed step-by-step solutions for each problem, making it unique for training AI in mathematical reasoning.

vs others: Unlike other datasets, MATH provides a structured approach to evaluating mathematical reasoning with competition-level problems and solutions.

via “benchmark dataset for evaluating mathematical reasoning in language models”

8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.

Unique: GSM8K uniquely combines linguistic diversity with multi-step reasoning challenges specifically tailored for language models.

vs others: Unlike other datasets, GSM8K focuses specifically on multi-step arithmetic problems that are challenging yet solvable by middle school students, providing a clear benchmark for AI capabilities.

via “commonsense reasoning benchmark dataset”

70K commonsense reasoning questions with adversarial distractors.

Unique: Utilizes adversarial filtering to ensure that incorrect options are specifically designed to mislead machines while remaining obvious to humans.

vs others: Offers a unique approach to commonsense reasoning evaluation that combines human-like accuracy with challenging adversarial examples, setting it apart from traditional datasets.

via “mathematical reasoning and step-by-step problem solving”

text-generation model by undefined. 1,37,84,608 downloads.

Unique: Qwen2.5-7B-Instruct includes explicit training on mathematical reasoning datasets (including GSM8K, MATH, and proprietary datasets) with emphasis on showing intermediate steps and justifying answers. The instruction-tuning includes prompts that encourage the model to 'think step by step' and 'show your work', which are known to improve mathematical reasoning through in-context learning effects.

vs others: Outperforms base Qwen2.5-7B on mathematical reasoning benchmarks by 15-20% due to instruction-tuning; more accessible than specialized math models (like Minerva) for general-purpose deployment

via “advanced mathematical problem evaluation”

Competition mathematics problems (harder than GSM8K)

Unique: MATH's dataset is specifically curated from high school math contests, providing a unique challenge that is more difficult than typical benchmarks, allowing for a clearer differentiation of model capabilities.

vs others: More challenging than GSM8K, making it a superior choice for evaluating advanced mathematical reasoning in AI models.

via “multi-step mathematical reasoning evaluation”

Grade school math problems requiring multi-step reasoning

Unique: GSM8K is specifically curated to include a diverse set of multi-step reasoning problems, making it more targeted than generic math datasets, allowing for precise evaluation of reasoning capabilities in LLMs.

vs others: More focused on multi-step reasoning than other benchmarks like MATH, which may include less structured problems.

via “mathematical reasoning and logic problem evaluation with specialized scoring”

ReLE评测：中文AI大模型能力评测（持续更新）：目前已囊括374个大模型，覆盖chatgpt、gpt-5.4、谷歌gemini-3.1-pro、Claude-4.6、文心ERNIE-X1.1、ERNIE-5.0、qwen3.6-max、qwen3.6-plus、百川、讯飞星火、商汤senseChat等商用模型， 以及step3.5-flash、kimi-k2.6、ernie4.5、MiniMax-M2.7、deepseek-v4、Qwen3.6、llama4、智谱GLM-5.1、MiMo-V2、LongCat、gemma4、mistral等开源大模型。不仅提供排行榜，也提供规模超200万的大

Unique: Evaluates mathematical reasoning with 1-5 quality scale for reasoning steps rather than binary correctness, enabling partial credit for correct methodology with computational errors. Combines final answer accuracy with reasoning quality assessment to capture mathematical thinking capability. Includes multi-step reasoning problems and logical inference tasks beyond simple arithmetic.

vs others: More nuanced mathematical assessment than MMLU (binary correctness) and captures reasoning quality vs answer-only evaluation

via “mathematical reasoning evaluation”

UGI-Leaderboard — AI demo on HuggingFace

Unique: Isolates mathematical reasoning as a distinct evaluation dimension on the leaderboard, enabling models to be ranked separately on math vs general generation, revealing capability specialization.

vs others: Simpler than running MATH or GSM8K locally with custom evaluation scripts, but less transparent than open-source math benchmarks regarding problem selection and difficulty.